{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:43:32Z","timestamp":1783611812754,"version":"3.55.0"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T00:00:00Z","timestamp":1629072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072212"],"award-info":[{"award-number":["62072212"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Development Project of Jilin Province of China","award":["20200401083GX"],"award-info":[{"award-number":["20200401083GX"]}]},{"name":"Development Project of Jilin Province of China","award":["2020C003"],"award-info":[{"award-number":["2020C003"]}]},{"name":"Development Project of Jilin Province of China","award":["2020LY500L06"],"award-info":[{"award-number":["2020LY500L06"]}]},{"name":"Guangdong Key Project for Applied Fundamental Research","award":["2018KZDXM076"],"award-info":[{"award-number":["2018KZDXM076"]}]},{"name":"Jilin Province Key Laboratory of Big Data Intelligent Computing","award":["20180622002JC]"],"award-info":[{"award-number":["20180622002JC]"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Human proteins that are secreted into different body fluids from various cells and tissues can be promising disease indicators. Modern proteomics research empowered by both qualitative and quantitative profiling techniques has made great progress in protein discovery in various human fluids. However, due to the large number of proteins and diverse modifications present in the fluids, as well as the existing technical limits of major proteomics platforms (e.g. mass spectrometry), large discrepancies are often generated from different experimental studies. As a result, a comprehensive proteomics landscape across major human fluids are not well determined.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To bridge this gap, we have developed a deep learning framework, named DeepSec, to identify secreted proteins in 12 types of human body fluids. DeepSec adopts an end-to-end sequence-based approach, where a Convolutional Neural Network is built to learn the abstract sequence features followed by a Bidirectional Gated Recurrent Unit with fully connected layer for protein classification. DeepSec has demonstrated promising performances with average area under the ROC curves of 0.85\u20130.94 on testing datasets in each type of fluids, which outperforms existing state-of-the-art methods available mostly on blood proteins. As an illustration of how to apply DeepSec in biomarker discovery research, we conducted a case study on kidney cancer by using genomics data from the cancer genome atlas and have identified 104 possible marker proteins.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability<\/jats:title>\n                  <jats:p>DeepSec is available at https:\/\/bmbl.bmi.osumc.edu\/deepsec\/.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab545","type":"journal-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T19:10:56Z","timestamp":1628881856000},"page":"228-235","source":"Crossref","is-referenced-by-count":15,"title":["DeepSec: a deep learning framework for secreted protein discovery in human body fluids"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1050-2808","authenticated-orcid":false,"given":"Dan","family":"Shao","sequence":"first","affiliation":[{"name":"Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University , Changchun 130012, China"},{"name":"College of Computer Science and Technology, Changchun University , Changchun 130022, China"},{"name":"Department of Computer Science and Engineering, University of Nebraska-Lincoln , Lincoln, NE 68588, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lan","family":"Huang","sequence":"additional","affiliation":[{"name":"Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University , Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4751-0708","authenticated-orcid":false,"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University , Changchun 130012, China"},{"name":"School of Artificial Intelligence, Jilin University , Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"He","sequence":"additional","affiliation":[{"name":"Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University , Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueteng","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Changchun University , Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Wang","sequence":"additional","affiliation":[{"name":"Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University , Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qin","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, College of Medicine, The Ohio State University , Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6581-6850","authenticated-orcid":false,"given":"Juan","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Nebraska-Lincoln , Lincoln, NE 68588, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,8,16]]},"reference":[{"key":"2023020108394247700_btab545-B1","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res"},{"key":"2023020108394247700_btab545-B2","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1373\/clinchem.2009.126706","article-title":"The clinical plasma proteome: a survey of clinical assays for proteins in plasma and serum","volume":"56","author":"Anderson","year":"2010","journal-title":"Clin. Chem"},{"key":"2023020108394247700_btab545-B3","doi-asserted-by":"crossref","first-page":"3387","DOI":"10.1093\/bioinformatics\/btx431","article-title":"DeepLoc: prediction of protein subcellular localization using deep learning","volume":"33","author":"Armenteros","year":"2017","journal-title":"Bioinformatics"},{"key":"2023020108394247700_btab545-B4","doi-asserted-by":"crossref","first-page":"2370","DOI":"10.1093\/bioinformatics\/btn418","article-title":"Computational prediction of human proteins that can be secreted into the bloodstream","volume":"24","author":"Cui","year":"2008","journal-title":"Bioinformatics"},{"key":"2023020108394247700_btab545-B5","doi-asserted-by":"crossref","first-page":"e16875","DOI":"10.1371\/journal.pone.0016875","article-title":"A computational method for prediction of excretory proteins and application to identification of gastric cancer markers in urine","volume":"6","author":"Hong","year":"2011","journal-title":"PLoS One"},{"key":"2023020108394247700_btab545-B6","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1093\/bib\/bbz160","article-title":"Human body-fluid proteome: quantitative profiling and computational prediction","volume":"22","author":"Huang","year":"2021","journal-title":"Brief. Bioinf"},{"key":"2023020108394247700_btab545-B7","doi-asserted-by":"crossref","first-page":"7574","DOI":"10.1038\/s41598-021-87204-z","article-title":"Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction","volume":"11","author":"Jain","year":"2021","journal-title":"Sci. Rep"},{"key":"2023020108394247700_btab545-B8","first-page":"250","article-title":"Therapeutic potential of the plasma proteome","volume":"5","author":"Lathrop","year":"2003","journal-title":"Curr. Opin. Mol. Ther"},{"key":"2023020108394247700_btab545-B9","doi-asserted-by":"crossref","first-page":"M111.009993","DOI":"10.1074\/mcp.M111.009993","article-title":"The human proteome project: current state and future direction","volume":"10","author":"Legrain","year":"2011","journal-title":"Mol. Cell. Proteomics"},{"key":"2023020108394247700_btab545-B10","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.mbs.2019.02.007","article-title":"A Novel Matched-pairs feature selection method considering with tumor purity for differential gene expression analyses","volume":"311","author":"Liang","year":"2019","journal-title":"Math. Biosci"},{"key":"2023020108394247700_btab545-B11","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1038\/2211056a0","article-title":"Two-dimensional resolution of plasma proteins by combination of polyacrylamide disc and gradient gel electrophoresis","volume":"221","author":"Margolis","year":"1969","journal-title":"Nature"},{"key":"2023020108394247700_btab545-B12","doi-asserted-by":"crossref","first-page":"D959","DOI":"10.1093\/nar\/gkt1251","article-title":"Plasma Proteome Database as a resource for proteomics research: 2014 update","volume":"42","author":"Nanjappa","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023020108394247700_btab545-B13","first-page":"D427","article-title":"The Pfam protein families database in 2019","volume":"47","author":"Sara","year":"2018","journal-title":"Nuclc Acids Res"},{"key":"2023020108394247700_btab545-B14","doi-asserted-by":"crossref","first-page":"1690","DOI":"10.1093\/bioinformatics\/btx818","article-title":"DeepSig: deep learning improves signal peptide detection in proteins","volume":"34","author":"Savojardo","year":"2018","journal-title":"Bioinformatics"},{"key":"2023020108394247700_btab545-B15","doi-asserted-by":"crossref","first-page":"4299","DOI":"10.1021\/acs.jproteome.7b00467","article-title":"The human plasma proteome draft of 2017: building on the human plasma PeptideAtlas from mass spectrometry and complementary assays","volume":"16","author":"Schwenk","year":"2017","journal-title":"J. Proteome Res"},{"key":"2023020108394247700_btab545-B16","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1109\/TNB.2015.2395143","article-title":"A computational method for prediction of saliva-secretory proteins and its application to identification of head and neck cancer biomarkers for salivary diagnosis","volume":"14","author":"Sun","year":"2015","journal-title":"IEEE Trans. Nanobiosci"},{"key":"2023020108394247700_btab545-B17","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1038\/092549a0","article-title":"Rays of positive electricity and their application to chemical analyses","volume":"92","author":"Thomson","year":"1914","journal-title":"Nature"},{"key":"2023020108394247700_btab545-B18","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1042\/bj0310313","article-title":"Electrophoresis of serum globulin: electrophoretic analysis of normal and immune sera","volume":"31","author":"Tiselius","year":"1937","journal-title":"Biochem. J"},{"key":"2023020108394247700_btab545-B19","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1002\/pmic.201400619","article-title":"N-terminal modifications of cellular proteins: the enzymes involved, their substrate specificities and biological effects","volume":"15","author":"Varland","year":"2015","journal-title":"Proteomics"},{"key":"2023020108394247700_btab545-B20","doi-asserted-by":"crossref","first-page":"e80211","DOI":"10.1371\/journal.pone.0080211","article-title":"Computational prediction of human salivary proteins from blood circulation and application to diagnostic biomarker identification","volume":"8","author":"Wang","year":"2013","journal-title":"PLoS One"},{"key":"2023020108394247700_btab545-B21","doi-asserted-by":"crossref","first-page":"18962","DOI":"10.1038\/srep18962","article-title":"Protein secondary structure prediction using deep convolutional neural fields","volume":"6","author":"Wang","year":"2016","journal-title":"Sci. Rep"},{"key":"2023020108394247700_btab545-B22","volume-title":"PUEPro: A Computational Pipeline for Prediction of Urine Excretory Proteins. Advanced Data Mining and Applications (ADMA)","author":"Wang","year":"2016"},{"key":"2023020108394247700_btab545-B23","doi-asserted-by":"crossref","first-page":"e9208","DOI":"10.15252\/msb.20199208","article-title":"Impact of C-terminal amino acid composition on protein expression in bacteria","volume":"16","author":"Weber","year":"2020","journal-title":"Mol. Syst. Biol"},{"key":"2023020108394247700_btab545-B24","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1109\/TCDS.2019.2924648","article-title":"Affective EEG-based person identification using the deep learning approach","volume":"12","author":"Wilaiprasitporn","year":"2020","journal-title":"IEEE Trans. Cognit. Dev. Syst"},{"key":"2023020108394247700_btab545-B25","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.cbi.2014.02.014","article-title":"UPLC\u2013MSE application in disease biomarker discovery: the discoveries in proteomics to metabolomics","volume":"215","author":"Zhao","year":"2014","journal-title":"Chem. Biol. Interact"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btab545\/39806874\/btab545.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/1\/228\/49006548\/btab545.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/1\/228\/49006548\/btab545.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T19:56:08Z","timestamp":1675281368000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/38\/1\/228\/6353025"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,8,16]]},"references-count":25,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btab545","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,1,1]]},"published":{"date-parts":[[2021,8,16]]}}}